The objectives of Project 1 (Classification and Integration) are to (a) continue research evaluating classification and measurement issues that affect the identification of children as learning disabled (LD), with a focus on response to instruction (RTI) models;and (b) expand use of meta-analytic methods to integrate research across domains of LD, including reading comprehension and executive functions.
Specific Aim 1 (Classification and Identification) addresses the nature and reliability of different methods for identifying children with LD based on simulated data and actual data from Projects 2-4. Simulations include (a) estimation of expected agreement when classification is made with multiple unreliable variables and various cut points;(b) identification of inadequate responders using different methods and cut points as well as other subgroups, such as so-called gifted LD or twice exceptional children;and (c) estimation of efficiency and utility in relation to different base rates and number of assessments. Similar comparisons will be made using actual data on intervention response from Project 3 (Intervention).
Specific Aim 2 (External Validity of RTI Models) addresses the validity of the intervention response criteria evaluated under Aim 1. Using data collected under Projects 2 and 4, we will compare adequate and inadequate responders from Project 3 (Intervention) on cognitive assessments of executive functions and other domains. We will work with Project 4 (Neuroimaging) to compare adequate and inadequate responders on structural and functional neuroimaging measures. In addition, intervention response criteria will be compared to criteria generated by other proposed classifications, including IQ-discrepancy, low achievement, and cognitive strengths and weaknesses models.
Specific Aim 3 (Integration) continues and expands our work on empirical synthesis from the previous five years. We propose to continue meta-analytic work addressing (a) predictors of intervention response;(b) executive functions in relation to different academic domains, especially reading comprehension and written expression;and (c) expand previous meta-analyses of intervention outcomes.

Public Health Relevance

Identifying individual children who meet criteria for LD has plagued research and practice. This proposal leverages advances in statistical computing and analytic models, simulation, and meta-analysis to continue and extend a long history of research on the classification and definition of LD, evaluating the reliability of different approaches to identification, the validity of classifications based on intervention response, and integration of research on classification, executive functions and intervention.

National Institute of Health (NIH)
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Specialized Center (P50)
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Special Emphasis Panel (ZHD1-DSR-H (53))
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University of Houston
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